Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge

Abstract

Current concepts of sensory processing in the cerebral cortex emphasize serial extraction and recombination of features in hierarchically structured feed-forward networks in order to capture the relations among the components of perceptual objects. These concepts are implemented in convolutional deep learning networks and have been validated by the astounding similarities between the functional properties of artificial systems and their natural counterparts. However, cortical architectures also display an abundance of recurrent coupling within and between the layers of the processing hierarchy. This massive recurrence gives rise to highly complex dynamics whose putative function is poorly understood. Here a concept is proposed that assigns specific functions to the dynamics of cortical networks and combines, in a unifying approach, the respective advantages of recurrent and feed-forward processing. It is proposed that the priors about regularities of the world are stored in the weight distributions of feed-forward and recurrent connections and that the high-dimensional, dynamic space provided by recurrent interactions is exploited for computations. These comprise the ultrafast matching of sensory evidence with the priors covertly represented in the correlation structure of spontaneous activity and the context-dependent grouping of feature constellations characterizing natural objects. The concept posits that information is encoded not only in the discharge frequency of neurons but also in the precise timing relations among the discharges. Results of experiments designed to test the predictions derived from this concept support the hypothesis that cerebral cortex exploits the high-dimensional recurrent dynamics for computations serving predictive coding.

Keywords: neuronal dynamics; predictive coding; rate codes; recurrent networks; temporal codes.

Fig. 1.

A cluttered scene, consisting of oriented contours (bottom), leads to activation of orientation-selective cells (blue circles) in input layer IV of V1. Because all contours have the same contrast and shape, the layer IV cells with corresponding RFs are assumed to be activated equally and to drive pyramidal target cells (–5) in layers II/III via ascending feed-forward connections (blue arrows). These pyramidal cells are reciprocally coupled through excitatory collaterals (red arrows) of axons (blue and orange arrows) projecting to layer IV target cells (a–e) of the next processing stage (V2). Pyramidal cells 3 and 4 are coupled more strongly than their neighbors because they have a shared preference for colinearly aligned contours of the same orientation (indicated by thicker recurrent collaterals). This enhanced coupling is due to prior exposure to elongated contrast borders that are frequent in natural environments (for details, see The Binding by Synchrony Hypothesis). Because of strong coupling, cooperativity between neurons 3 and 4 is enhanced, and their responses become more coherent (indicated by orange color of feed-forward axons). This, in turn, leads to enhanced responses in target cells c and d, because cortical cells are driven more effectively by synchronous than temporally dispersed excitatory postsynaptic potentials (EPSPs) (–53, 228). In addition, the discharges of neurons c and d are likely to be more synchronized, as well. In this way, recurrent coupling that captures the statistical regularities of natural environments can be exploited to detect relations among features, to convert these relations into temporal relations among discharges that, in turn, impact the spike responses of input cells at the next processing stage.

Fig. 2.

Simplified graph of recurrent connections among functional columns in layers II/III of the primary visual cortex. Nodes with similar colors respond to features that frequently occur together and are therefore coupled more strongly than other nodes (thick lines).